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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JURNAL PENDIDIKAN TAMBUSAI Jurnal Informasi dan Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Wahana Pendidikan Society: Jurnal Pengabdian Masyarakat
Yusup, Dadang
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Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : Jurnal Informatika dan Teknik Elektro Terapan

 1 
STUDI KOMPARASI ALGORITMA RANDOM FOREST CLASSIFIER DAN SUPPORT VECTOR MACHINE DALAM PREDIKSI PENYAKIT JANTUNG Alfajr, Nur Halizah; Garno, Garno; Yusup, Dadang
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6569

Abstract

Heart disease is a non-communicable disease with a high mortality rate both globally and in Indonesia. According to WHO, around 17.9 million deaths occur each year due to cardiovascular diseases. Early prediction is crucial to reducing mortality and improving life expectancy. This study compares the performance of machine learning algorithms Random Forest Classifier and Support Vector Machine in predicting heart disease. The dataset consists of 5432 medical records from cardiac outpatients at RSUD Kabupaten Bekasi in 2024, with two classes (labeled 1 (heart disease) = 3068 and labeled 0 (non-heart disease) = 2364). Models were developed using the Knowledge Discovery in Databases (KDD) approach. Evaluation results show that the Support Vector Machine model achieved the best performance compared to Random Forest Classifier with 65% accuracy, 70% precision, 68% recall, and 64% f-measure. Cross-validation and ROC analysis also indicated that Support Vector Machine obtained the highest AUC score, ranging from 0.67 to 0.68, which is categorized as poor classification.
SISTEM INFORMASI RESERVASI PADA RUMAH MAKAN PADANG SITI NURBAYA BERBASIS WEBSITE Ubaid, Harfa Sabri; Yusup, Dadang
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6600

Abstract

Rumah makan tradisional seperti Rumah Makan Padang Siti Nurbaya menghadapi tantangan dalam mengelola reservasi meja yang masih dilakukan secara manual, menyebabkan ketidakefisienan dan potensi kekecewaan pelanggan. Penelitian ini bertujuan untuk mengembangkan sistem informasi reservasi berbasis website yang dapat memudahkan proses pemesanan tempat serta menyajikan informasi menu secara komprehensif. Metode pengembangan yang digunakan adalah Waterfall, dengan tahapan analisis kebutuhan, desain, implementasi, pengujian, dan pemeliharaan. Hasil implementasi menunjukkan sistem berhasil memenuhi kebutuhan fungsional seperti formulir reservasi online, pengelolaan data reservasi, dan pengelolaan menu makanan. Pengujian melalui blackbox testing menunjukkan semua fitur berfungsi sesuai harapan, sedangkan User Acceptance Testing (UAT) menghasilkan persentase kelayakan sebesar 79,6%. Sistem ini berhasil mengatasi permasalahan manajemen reservasi manual, meningkatkan efisiensi operasional restoran, serta memberikan pengalaman pemesanan yang lebih baik bagi pelanggan.
Co-Authors Ahmad Fauji, Ahmad Alfajr, Nur Halizah Ana Tasya, Chintya Apriade Voutama Atifah, Salwa Nur Carudin, Carudin Fadhli Adam, Muhammad Fajriansyah, Annas garno, Garno Hutahaean, Jonatan Iqbal Maulana Luthfi Khairullah, Muhammad Novalia, Elfina Nur Ibrahim Nur Sallamah, Afifah Prihandani, Kamal Prihandini, Kamal Purwantoro Rizal, Adhi Rosalina, Dina Satrio, Adi Sufah, Feila Suha, Anna Lathifah Susilawati, Susilawati Susilo Yuda Irawan, Agung Ubaid, Harfa Sabri Yuniar, Alvi